The Data Scientist Who Disappeared: On Ego, Emptiness, and Finding Clarity in Cognitive Chaos

What Buddhist monks, psychedelic philosophers, Beat poets, and your neural network have in common.

There’s a particular kind of exhaustion that comes from working in cognitively demanding fields. Data scientists know it well. So do researchers, analysts, engineers, and anyone whose job involves wrestling complex abstractions into submission eight hours a day.

It’s not physical fatigue. It’s something stranger — a kind of mental congestion, like you’ve been running too many processes and forgotten to clear the cache. Your brain becomes a cramped room full of half-finished models, provisional conclusions, and the nagging suspicion that you’ve overlooked something important.

I spent years in that room.

Charles Bukowski, who knew something about sitting with discomfort, wrote: “What matters most is how well you walk through the fire.” He wasn’t talking about data science. He was talking about life. But for those of us who spend our days in cognitive fire — the relentless heat of complex problems, tight deadlines, and the pressure to produce insight on demand — the question of how we walk through it becomes everything.

The Problem With Being Someone

Here’s what they don’t teach you in statistics courses: the biggest obstacle to clear thinking isn’t inadequate training or insufficient data. It’s you. Or more precisely, it’s your attachment to being you — the expert, the person who needs to have answers, the professional whose identity is wrapped up in being right.

Buddhism has a name for this obstacle: ego-clinging.

Chögyam Trungpa Rinpoche, the Tibetan teacher who scandalized and illuminated the American spiritual scene in equal measure, called it “spiritual materialism” — but the concept extends far beyond spirituality. We turn everything into material for the ego’s construction project. Our insights become my insights. Our models become proof of my intelligence. Even our failures become my dramatic struggles.

“Ego is constantly attempting to acquire and apply the teachings of spirituality for its own benefit,” Trungpa wrote. Substitute “spirituality” with “data science” or “analytical thinking” and the diagnosis still holds. We collect techniques, methodologies, and frameworks not to see more clearly, but to become someone who sees clearly. The subtle difference is everything.

The ego isn’t inherently bad. It’s useful software — it helps you navigate the social world, maintain professional relationships, advocate for your ideas. But like any software running in the background, it consumes resources. And in cognitive work, those resources are precious.

Every time you approach a dataset, you bring your ego along. It whispers: I should have seen this pattern by now. What if my model is wrong? What will my colleagues think? A real data scientist would already know this.

This internal narrator doesn’t help you see the data more clearly. It adds noise. It introduces bias — not the statistical kind you can measure, but something more insidious: the bias of needing to protect and affirm your self-image.

The View From Nowhere

The Buddhist concept of anattā — often translated as “non-self” or “emptiness of self” — sounds nihilistic to Western ears. It isn’t. It’s liberating.

What the Buddhist contemplatives discovered, through thousands of years of rigorous investigation into the nature of mind, is that the “self” we experience isn’t a fixed entity. It’s a process. A story we tell ourselves, moment by moment, using memory, projection, and narrative.

Thich Nhat Hanh, the Vietnamese Zen master whose gentleness carried the weight of someone who had witnessed war and chosen peace anyway, offered a more accessible frame. He spoke of “interbeing” — the recognition that nothing exists independently, that everything inter-is with everything else.

“If you are a poet,” he wrote, “you will see clearly that there is a cloud floating in this sheet of paper. Without a cloud, there will be no rain; without rain, the trees cannot grow; and without trees, we cannot make paper.”

Apply this to analytical work: there is no “you” who exists separately from the data you’re analyzing. The data arose from systems you’re embedded in. Your capacity to analyze it emerged from education, from mentors, from countless thinkers whose work you’ve absorbed. Even your confusion is not purely yours — it’s the confusion of a species still learning to make sense of complexity.

When you meditate — really meditate, not just sit there thinking about meditating — you start to notice something curious: you can watch thoughts arise without claiming ownership of them. You can observe emotions without becoming them. The watcher and the watched begin to merge into simple awareness.

Jiddu Krishnamurti, the Indian philosopher who refused to be anyone’s guru, spent decades pointing at this. “The observer is the observed,” he repeated in lecture after lecture, frustrating audiences who wanted something more complicated. But that’s the whole teaching. When you truly see that the one who is looking and the thing being looked at are not separate, the friction between self and world dissolves.

This isn’t mystical hand-waving. It’s phenomenologically observable. And it has profound practical implications.

When you let go of the ego’s desperate need to be the smart one, the capable one, the one who doesn’t make mistakes, something remarkable happens: you can finally see. Not through the lens of self-protection, but directly. You become, in a sense, nobody — and suddenly the data speaks more clearly because you’re no longer shouting over it with your own anxieties.

The Universe Looking at Itself

The philosopher and psychonaut Terence McKenna had a provocative way of describing this shift. He suggested that we are not simply observers of the universe — we are the universe observing itself.

“We are the cosmos attempting to understand itself,” he wrote. “Through us, the universe gazes back at its own face.”

This isn’t just poetry, though it is that too. It’s a reframe that collapses the artificial boundary between observer and observed — the same boundary that causes so much cognitive friction in analytical work.

Alan Watts, who spent his life translating Eastern philosophy for Western minds, put it this way: “Through our eyes, the universe is perceiving itself. Through our ears, the universe is listening to its harmonies. We are the witnesses through which the universe becomes conscious of its glory, of its magnificence.”

Think about what happens when you analyze data. There’s you (the subject), and there’s the dataset (the object). You’re trying to extract truth from it while standing outside it. This dualistic setup creates friction. You fight the data. You force interpretations. You get frustrated when it doesn’t cooperate.

But what if you’re not separate from what you’re studying? What if, as McKenna suggested, you are the same universe that generated this data, now folding back on itself in an attempt at self-understanding?

This perspective dissolves the adversarial relationship between analyst and analysis. You stop trying to conquer the data and start participating in a collaborative act of revelation.

Rumi, the 13th-century Sufi poet, knew this dissolution intimately: “You are not a drop in the ocean. You are the entire ocean in a drop.” The data scientist who truly absorbs this doesn’t become less rigorous. They become less defended. And in that openness, patterns reveal themselves that the defended mind could never see.

Meditation as Cognitive Hygiene

Let me be concrete about what I’m proposing.

Meditation isn’t an escape from analytical work. It’s preparation for it. The practices that Buddhist monks have refined over millennia — particularly śamatha (calm abiding) and vipassanā (insight meditation) — are essentially techniques for training attention and dissolving cognitive distortions.

Thich Nhat Hanh emphasized the radical simplicity of this: “Feelings come and go like clouds in a windy sky. Conscious breathing is my anchor.”

Twenty minutes of focused attention practice each morning does something measurable: it improves your ability to sustain concentration, reduces the cognitive load of emotional self-regulation, and creates space between stimulus and response. That space is where good thinking happens.

When you sit with your breath and notice how desperately the mind wants to do anything other than stay present — planning, remembering, catastrophizing, fantasizing — you’re building the same muscle you need to sit with a confusing dataset and resist the urge to jump to premature conclusions.

The letting go that happens in meditation — letting go of thoughts, of the need to control, of the running commentary — trains you to let go in your work. To let go of your attachment to being right. To let go of the first hypothesis that seems plausible. To let go of your elegant model when the evidence says it’s wrong.

Bukowski, in his rough-edged way, understood the necessity of this release: “The problem with the world is that the intelligent people are full of doubts, while the stupid ones are full of confidence.” But here’s the deeper cut: it’s not intelligence versus stupidity. It’s the willingness to hold your intelligence lightly, to doubt your own certainties, to stay humble before the data even when you’ve been right before.

Mary Oliver, the poet of close attention, described the quality of presence that meditation cultivates: “Instructions for living a life: Pay attention. Be astonished. Tell about it.” This is also a pretty good methodology for data science. Pay attention — really attend to what’s in front of you. Be astonished — let the patterns surprise you instead of forcing them into familiar shapes. Tell about it — communicate what you’ve found with clarity and honesty.

Releasing Expectations

There’s a concept in Zen Buddhism called shoshin — beginner’s mind. It refers to an attitude of openness, eagerness, and lack of preconceptions, even when studying at an advanced level.

“In the beginner’s mind there are many possibilities,” wrote Shunryu Suzuki, “but in the expert’s mind there are few.”

This is lethal for data work. The moment you decide you know what the data will show, you’ve compromised your ability to see what it actually shows. Your expectations become a filter, and the signal that contradicts your priors gets classified as noise.

Trungpa Rinpoche called this “idiot compassion” when it applies to helping others — but there’s an “idiot expertise” that applies to analytical work. It’s the expertise that’s so sure of itself that it can’t learn anything new. It’s the model that fits the last problem perfectly and therefore gets misapplied to the next one.

Beginner’s mind doesn’t mean feigning ignorance or abandoning your training. It means holding your expertise lightly. It means approaching each new problem as if it might be completely unlike any problem you’ve seen before — because it might be.

Lao Tzu, in the Tao Te Ching, pointed at this two and a half millennia ago: “In the pursuit of learning, every day something is acquired. In the pursuit of Tao, every day something is dropped.” The accumulation of knowledge is necessary. But so is the release of certainty. Both movements — acquiring and dropping — must continue in parallel.

This is harder than it sounds. Your brain wants to pattern-match. It wants to shortcut to the familiar. Every time you’ve been rewarded for expertise, you’ve reinforced the habit of arriving with answers rather than questions.

Meditation practice helps here too. The instruction to “begin again” that you hear in many guided meditations — returning to the breath each time the mind wanders, without judgment — trains exactly this capacity. You practice, over and over, the art of starting fresh.

Cutting Through

Trungpa’s most famous book is called Cutting Through Spiritual Materialism, and the metaphor of cutting is essential. The ego doesn’t dissolve through gentle persuasion. It requires a sword.

Not violence — precision. The willingness to cut through your own bullshit. To see when you’re performing insight rather than having it. To notice when you’re defending a position because it’s right versus because it’s yours.

Ikkyu, the 15th-century Zen master known for his wild poetry and wilder behavior, wrote: “That stone Buddha deserves all the birdshit it gets. I wave my skinny arms like a tall flower in the wind.” He was pointing at the danger of making anything — even enlightenment, even analytical rigor — into a dead idol. The moment your methodology becomes a stone Buddha, it starts accumulating birdshit.

The German poet Rilke, in his Letters to a Young Poet, offered advice that applies equally to young data scientists: “Be patient toward all that is unsolved in your heart and try to love the questions themselves, like locked rooms and like books that are now written in a very foreign tongue.”

Love the questions. Not as obstacles to answers, but as the living heart of the work. The best analysts I know are the ones who remain genuinely curious, who treat each unsolved problem as an invitation rather than an irritation.

The Clearing

Here’s what it feels like when these practices click:

You sit down to work. The usual anxious chatter — am I good enough, what if I fail, what will they think — is still there, but it’s quieter. You notice it the way you’d notice traffic noise outside your window. It’s happening, but it’s not happening to you.

You look at the problem. Not as an adversary to defeat, not as a test of your worth, but as something interesting. As the universe — which includes you — trying to understand a part of itself.

You don’t need to have the answer immediately. You don’t need to be brilliant. You can just look. You can follow the data where it goes, even if where it goes is unexpected, even if it contradicts your hypothesis, even if it makes your previous work irrelevant.

Jack Kerouac, who studied Buddhism between benders and road trips, wrote in The Dharma Bums: “One day I will find the right words, and they will be simple.” The same applies to analysis. One day you find the right model, and it is simple. Not because you forced simplicity onto complexity, but because you waited, attended, let the essential pattern emerge.

This is the clearing. The space that opens when you stop cluttering your cognition with self-referential noise.

From this place, insights come faster. Not because you’re smarter, but because you’re clearer. You’ve removed the obstacle of yourself.

Leonard Cohen, who spent years in a Zen monastery on Mount Baldy, sang: “There is a crack in everything. That’s how the light gets in.” Your models will have cracks. Your understanding will be incomplete. This is not a flaw to be eliminated but the very opening through which insight enters.

Practical Notes

If this resonates, here’s where to start:

Sit every day. Even ten minutes matters. Consistency beats duration. The point isn’t to achieve some special state but to practice the mechanism of attention — noticing when the mind has wandered and gently returning to the object of focus. As Thich Nhat Hanh taught: “The present moment is filled with joy and happiness. If you are attentive, you will see it.”

Study the philosophy. Not as dogma, but as a set of maps drawn by serious explorers of consciousness. Start with Shunryu Suzuki’s Zen Mind, Beginner’s Mind, Pema Chödrön’s When Things Fall Apart, Thich Nhat Hanh’s The Miracle of Mindfulness, or Trungpa Rinpoche’s Cutting Through Spiritual Materialism. For the McKenna perspective, his Food of the Gods and recorded lectures offer a wilder, more psychedelic angle on similar territory. Alan Watts’ The Way of Zen remains a beautiful bridge between traditions.

Read the poets. Rumi, Rilke, Mary Oliver, Bukowski, Ikkyu. Poetry trains a different kind of attention than analytical prose. It teaches you to hold paradox, to sit with ambiguity, to let meaning arrive sideways. These are essential cognitive skills that technical training rarely develops.

Notice when ego is driving. In meetings, in analysis, in how you react to feedback. Not to judge yourself — that’s just more ego — but to see the mechanism. Awareness is itself transformative. Krishnamurti: “The ability to observe without evaluating is the highest form of intelligence.”

Let go of outcomes. This doesn’t mean don’t work hard or don’t care about quality. It means do your best work and release attachment to how it’s received. The Bhagavad Gita’s instruction — “You have a right to your actions, but never to your actions’ fruits” — applies beautifully to data work. Control what you can control. Release the rest.

Embrace not-knowing. Trungpa spoke of the “wisdom of uncertainty.” The willingness to say “I don’t know” is not weakness — it’s the precondition for genuine discovery. As Bukowski wrote: “The shortest distance between two points is often unbearable.” Sometimes the honest path through a problem requires sitting with discomfort rather than rushing to false resolution.

The Ultimate Analysis

Terence McKenna proposed that the universe is engaged in a vast project of novelty-creation — that existence tends toward increasing complexity, interconnection, and self-reflection, culminating in moments of radical insight where the cosmos suddenly sees itself more clearly than it did before.

Maybe that’s grandiose. Or maybe it’s exactly right.

Either way, the work we do in analytical fields has this quality: we are consciousness attempting to understand the patterns of existence. We are the universe building tools to study itself — regression models, neural networks, Bayesian frameworks — each one a lens that lets us see dimensions of reality that were previously invisible.

Thich Nhat Hanh, with characteristic simplicity, taught: “We are here to awaken from our illusion of separateness.” The illusion that we are separate from our data. Separate from our colleagues. Separate from the systems we study. When that illusion softens, analysis becomes something closer to communion.

When we clear away the distortions of ego, when we release our death-grip on being right, when we approach our work with the openness of a beginner and the depth of a contemplative, we become better instruments for this cosmic project of self-understanding.

The data scientist who learns to disappear — to dissolve the artificial boundary between self and analysis, observer and observed — doesn’t become less effective. They become a clearer channel.

And in that clarity, both the work and the worker are transformed.

The quieter you become, the more you can hear. — Ram Dass

And now that you don’t have to be perfect, you can be good. — John Steinbeck, East of Eden

About the Author: Brian Curry is the creator of PyCausalSim, Papilon and an AI, Data, and ML Scientist, analytics and Data Science professional with over 20 years of experience across enterprise companies, and founder of Vector1 AI, a part time side project developing tools and systems to move from correlation-based analytics to causal decision-making.